[1]} proposed an end-to-end coreference resolution model by directly considering all spans in a document as potential mentions and learn distributions over possible antecedents for each. The introduced model computes span embeddings that combine context-dependent boundary representation with a head-find attention mechanism. Their span-ranking approach is similar to mention ranking algorithms, but reasoning over a larger space by jointly detecting mentions and predicting coreference. In our paper, we also refer to a similar span representation structure as given in this work.
